Dataframes - Part 01
Summary
TLDRThis tutorial introduces the Pandas library in Python, widely used for data manipulation and analysis. It begins with instructions on installing and importing Pandas, followed by an explanation of DataFrames, a key object that functions like a table similar to Excel or SQL. The tutorial demonstrates how to load data from various sources like CSV files and SQL databases, and provides a hands-on example of reading and manipulating a CSV file. Additionally, it touches on the integration of Pandas with machine learning libraries like Scikit-learn for data analysis and modeling.
Takeaways
- 🐍 Python's pandas library is essential for data manipulation.
- 📚 You can install pandas via pip if it's not included with Python.
- 🔌 Importing pandas is done using 'import pandas as pd'.
- 📊 A DataFrame is the primary object in pandas, akin to a spreadsheet or SQL table.
- 💾 DataFrames are optimized for data aggregation and calculations.
- 🔄 DataFrames can be inputted into machine learning models, not just arrays.
- 🌐 Data can be sourced from various formats like CSV, Excel, SQL, and APIs.
- 📁 The script demonstrates how to read a CSV file into a DataFrame.
- 🔍 The script shows how to specify the path to a file for data reading.
- 📈 The script also touches on loading datasets from libraries like scikit-learn.
Q & A
What is pandas and why is it important for data manipulation?
-Pandas is a Python library that provides data structures and data analysis tools for Python programs. It is widely used for data manipulation and analysis because it allows for efficient and easy handling of structured data.
How can you install pandas if it's not already available in your Python environment?
-You can install pandas via the terminal using the command 'pip install pandas'.
What is the typical way to import pandas in a Python script?
-The typical way to import pandas is by using the line 'import pandas as pd'.
What is a DataFrame in pandas?
-A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It is similar to a table or a spreadsheet and is the primary data structure used in pandas.
How does pandas relate to Excel and SQL?
-Pandas can perform operations similar to both Excel and SQL. It allows for data manipulation like Excel and can execute operations similar to SQL queries.
Can pandas DataFrames be used in machine learning models?
-Yes, pandas DataFrames can be used as input for machine learning models. You can provide the DataFrame as an entry instead of an array.
What are some different data sources from which pandas can read data?
-Pandas can read data from various sources including CSV files, APIs, SQL queries, Excel files, and clipboard.
How do you read a CSV file into a pandas DataFrame?
-You can read a CSV file into a DataFrame using the function 'pd.read_csv(filepath)' where 'filepath' is the location of the CSV file.
What does the 'pd.read_csv()' function do?
-The 'pd.read_csv()' function reads a comma-separated values (CSV) file into a pandas DataFrame.
How can you specify the separator in a CSV file when reading it into a DataFrame?
-You can specify the separator in a CSV file by using the 'sep' parameter in the 'pd.read_csv()' function. For example, if the separator is a tab, you would use 'pd.read_csv(filepath, sep='\t')'.
What does the 'pd.read_csv()' function return?
-The 'pd.read_csv()' function returns a DataFrame object containing the data from the CSV file.
Can you provide an example of how to load a dataset from a machine learning library using pandas?
-Yes, you can load datasets from libraries like scikit-learn using pandas. For example, you can load the diabetes dataset from scikit-learn using 'from sklearn.datasets import load_diabetes' and then convert it to a DataFrame.
Outlines
🐍 Introduction to Pandas
The speaker introduces the audience to the Pandas library in Python, which is used for data manipulation. They explain that Pandas is typically installed with Python, but can be installed via terminal using the command 'pip install pandas'. The main object in Pandas is the DataFrame, which is likened to a table and can be used for operations similar to those in Excel or SQL. The speaker emphasizes the practicality and widespread use of DataFrames in various programming languages like R, and mentions their utility in machine learning and regression tasks. They also touch on the ability to read data from various sources such as CSV, APIs, SQL, and Excel files.
📂 Reading Data into DataFrames
The speaker demonstrates how to read data from a CSV file into a DataFrame. They guide through the process of creating a Google Sheet, downloading it as a CSV, and then moving and renaming the file for easier access. The path to the CSV file is identified, and the speaker shows how to use the 'pd.read_csv()' function in Pandas to load the data into a DataFrame. The DataFrame structure is explained, including headers and index columns, and the speaker saves the loaded content into a variable named 'restaurants'. They also mention the ability to read data from other file types and sources like Google BigQuery, JSON, and pickle files.
📊 Loading Datasets in Pandas
The speaker discusses the process of loading datasets into Pandas, specifically mentioning the 'seaborn' library, which is used for machine learning. They explain how to access different datasets provided by the library and load them using the 'load_dataset()' function. The focus is on working with a specific dataset, and the speaker provides a brief overview of the datasets available. The music in the background suggests an informal and engaging tone to the tutorial.
Mindmap
Keywords
💡pandas
💡data frame
💡CSV
💡install
💡terminal
💡import
💡read
💡Google Sheets
💡SQL
💡machine learning
💡regression
Highlights
Introduction to pandas as a Python library for data manipulation.
How to install pandas via terminal using the command 'pip install pandas'.
Importing pandas in Python with 'import pandas as pd'.
Pandas is widely used for working with data in Python.
DataFrame is the main object manipulated in pandas, similar to a table.
Pandas allows for both SQL-like and Excel-like operations on data.
DataFrames are practical for data aggregation and calculations.
DataFrames can be used as inputs for machine learning libraries.
Pandas can read data from various sources like CSV, APIs, SQL, etc.
Demonstration of reading data from a CSV file.
Explanation of how to specify the correct file path for data reading.
Renaming files for easier reference when working with data.
Using the 'pd.read_csv()' function to load CSV data into a DataFrame.
The importance of specifying the correct separator in the read function.
Visual representation of how data is loaded into a DataFrame.
Different sources from which pandas can read data, including Google BigQuery and JSON files.
Loading datasets from the 'seaborn' library for analysis.
Overview of the various datasets available for loading in pandas.
Transcripts
[Music]
hello everyone and welcome to the part
of data frame in pandas so uh pandas is
a library in Python and this library is
enabled us to getting all data in a nice
way
um so how does it work and what do we
need so we need to import this Library
called pandas
I normally it's like installed when we
installed python but you might need to
install it via the terminal so to
install it viazo terminal you open your
terminal and you do this command pip
install pandas so first try to run this
line in your notebook this import pandas
aspd so this is uh what we're going to
do so here we do
um import
pandas
aspd
so this is what we want to do and this
works right so if you have a mirror then
you will open your terminal
you will open your terminal uh in your
terminal I'm opening it
uh in your terminal uh you will do
something like peeping Style
Pippin style pandas if you need uh so I
don't need to did it I don't need to do
it so I'm not running this command so
pandas is a library for data frame it is
the main library that is used in Python
widely to work with data
um so the main objects that we
manipulate when we work with pandas is
called a data frame a data frame is a
bit like a table and on this table you
can do operation as a parallel that we
can draw uh pandas will work a bit like
an Excel table
uh so it's a mix I would say between
Excel and SQL where you can do some SQL
like operation and you can do some Excel
operation so how does it look like so uh
pandas is a way to manipulate data Frame
data frame is an object you will tell me
okay it's an object but what kind of
object so it is an object that is a
quite particular right
so it is um an object but in Python
everything is object and it enables us
to work with tables in Python it is very
practical it is also widely used so it
is a type of data that is also used if
you work in another programming language
let's say R you will also manipulate
data frame in R and it's very nice
because there is a lot of functions that
are already optimized to work with this
kind of data so if you want to do some
aggregation you want to do some
calculation on your data this is very
nicely optimized with data frame
um and uh let's say you were working
with library to do machine learning to
do your regression Etc you can also
provide this data frame as an entry so
widely uh if you use psychic learn or
for like machine learning or you use
like uh other stuff to do regression
like we've seen before this type model
you can enter instead of array you can
enter the colon of your data frame as
inputs so this is also some things that
is possible with data frame
um so yes uh we need to get data uh but
from where you know so uh there is
different kind of data that exist right
you can get data from internet you can
have data saving an Excel file etc etc
um so we've append us we are able uh to
read data from different sources so we
can read data from CSV from an API from
SQL clipboard etc etc so I'm going to
demonstrate uh or you can do all this
kind of different stuff so if you manage
to establish a connection with SQL then
you can read data from some SQL queries
right
I'm just going to show you how it works
um with like digged uh etc etc so
um
um this is how it looks and uh yeah so
pandas is looking like PD so I can do
like PD dots um
from PD Dot read so if I do PD read I do
see I read CSV I read my Excel Etc so if
I have a csvs and I will put the path to
my stuff so if I have let's say
table.csv somewhere table dot CSV
somewhere it will work so we can do this
together so let's say here I have a
jamboard but what I will go I will go to
my drive
drive and I will create a Google sheet I
could create a Google chat
um so I will have you let's say we're
going to go back with a restaurant uh
and I have restaurant uh one up and then
I will have a city and I will have Paris
and then I have uh restaurants two and
three that's it and then I will have
let's say my rating or here I have like
three four and five and here you have
Paris London
uh in room
okay so we have that I know I give it a
name I call it like restaurants
restaurants up I know I can do download
right so I can download a CSV
uh so I'm downloading my CSV it's called
restaurant right uh and now uh I can go
in my download and I know it is there
right so I know it is uh in my download
uh so what I'm gonna do uh from my
download to I'm gonna move it
um I'm gonna uh check what is a path of
it so I know like uh my new restaurant
here it has a certain path so I'm
looking to my path I know it's like oh
uh Macintosh
I'm like okay cool
um so no I'm go there and I can copy
paste this link so I know I go to like
users
uh Morgan
download and it's called Uh
the restaurant I'm going to really meet
restaurants you know so it's like easier
because it has a weird name so I'm just
gonna rename it like restaurants dot CSV
so this is I just remembering renaming
renaming my stuff
use.csv uh so yeah I have my CSV and
it's called restaurants restaurants
[Music]
torrents.csp
so I go do something something like this
oh you have to go back to like users and
stuff
um yeah so we need to like specify uh
the right path uh Etc
um yeah so this is uh how um it will uh
work basically you will specify your
file I would just copy it
in my uh where I got it and here I can
now open my restaurants
yep so here it works you know uh and I
do see that aperture tabulation so I can
put a step and I think I need to put tab
or something like yeah t
things this is how we do uh yeah so you
know I choose to download a CSV that was
with a separation with tabulation but if
I have a CSV which separation is a coma
I will put this like in this function
the separator but in my case I go to
tabulation so I'm using this and you do
see here it looks a bit like the stuff I
have online right so here is like my
headers
oh my headers
yeah you have like three column but I
also have like three rows and here I
have like zero one two and it's like
zero one two correspond to not truly to
this but it's a bit the same so it's
like an index so this will be equal to
Index this would be called My First
Column second column third column there
are three rows so this will be my um
table so I could be like DF uh it's to
data frame we usually call them DF and
if I know to like GF restaurants it's
that one so I can save the content of
this I just read it I save it in a
variable uh I know I have it here as a
GF restaurant so this is where I reread
it from CSV and uh I've seen before you
know when we do read uh we do see like
this is for Google bigquery this is from
a Json file this is from a pickle file
see from s is query table whatever
XML stutter so you have all the
different sources and you have even more
available
so now we're gonna work uh with data
sets so it's like some data sets that
already exist uh so we're just going to
do uh from Escalon
that's it
that's it's important yeah
from SQL and imported as a victim I can
do this so I can do this and then I can
do dataset dot lot uh David so you have
like is this is just to lot something uh
and then I can do di figure a lot
diabetes so this is just
um a function
um uh where I'm just gonna learn this is
not very important it's just like I'm uh
in in this Library uh so you're gonna
see
um the app is looking like this
um so in this
um in in uh so we want to load uh
something from
um Escalade so Escalade is a machine
learning library if you want per
um Excellence uh
um and then
um there is a different data items that
are provided so if you read a year you
know if I just go to like this data set
thing if I just go to the data set and I
do dot something
um I can just see in load I have loads
um of uh different uh data sets that are
imported uh so yeah you could you could
see them here basically uh so these are
all the different possible data sets uh
and then we can load Zen basically
uh so this is how it works and we might
mainly going to work with this data set
here
[Music]
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